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A Dynamic Learning Model (DLM) employs a hybrid AI approach to learning and responding to queries, enabling it to become smarter and more knowledgeable in addressing various FAQs related to a topic it has been trained on. Additionally, it can perform non-ambiguous arithmetic and conversion problems while executing a Chain-of-Thought (CoT)

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Python SQLite

Dynamic Learning Model

The Dynamic Learning Model (DLM) is a hybrid AI system designed to learn, adapt, and intelligently respond to user queries. It combines natural language understanding with structured reasoning to improve over time as it interacts with users.

Key capabilities include:

  • FAQ Handling: Learns and responds to frequently asked questions based on the knowledge it has been trained on.

  • Chain-of-Thought (CoT) Reasoning: Performs clear, step by step logic to solve non-ambiguous arithmetic and unit conversion problems.

  • Custom Knowledge Integration: DLM is fully extensible. You can initialize it with an empty SQL database and train it with your own domain-specific knowledge.

Whether you're building a student support bot, a domain-specific assistant, or an adaptive Q&A system, DLM offers a flexible foundation to power your intelligent applications

  • This model uses SpaCy, SQLite, & NLTK for many of its functions

NOTICE: This is a public package, to install it, run: pip install dynamic-learning-model

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About

A Dynamic Learning Model (DLM) employs a hybrid AI approach to learning and responding to queries, enabling it to become smarter and more knowledgeable in addressing various FAQs related to a topic it has been trained on. Additionally, it can perform non-ambiguous arithmetic and conversion problems while executing a Chain-of-Thought (CoT)

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